Methods and systems for data management and analysis

ABSTRACT

Provided are methods comprising receiving a query for information from the database, determining particular data element types and data element values that are the subject of the query, instantiating a query data structure containing the data element types and the data element values that are the subject of the query, identifying records within the database that contain one or more data element types and/or data element values that are included in the query data structure, and instantiating a results data structure comprising information relating to the identified records.

BACKGROUND

The ability to obtain and analyze pertinent information from largedatabases is a critical element in understanding what is happening in abusiness. Businesses are collecting more and more data as theiroperations increase in size and complexity. Identifying and locatingrelevant data in these voluminous databases continues to be asignificant challenge that is made substantially more complex anddifficult as the sizes and complexities of the databases have grown.This process, as well as the process of analyzing and visualizing therelevant data, are computationally and time intensive.

Data analysis or analytics is a process of inspecting, cleaning,transforming, modeling and presenting the data with the goal ofhighlighting useful information, suggesting conclusions, and supportingdecision making. As part of this process it is often useful to take whatis referred to as a “snapshot” of a data presentation. The term wascoined as an analogy to the same term as used in photography, and likeits photographic counterpart, a data snapshot is a static representationof certain elements at the particular point in time and state that thesnapshot was taken. Such elements as are included in a data snapshot mayinclude the results of applying various queries or other processing ofthe underlying data.

While database snapshots and the techniques therefor are known to thoseskilled in the database art, they currently have several limitations.Snapshots are static and reflect the data and/or the visualization ofthe data at a particular state and point in time. Snapshots can becreated, stored, and called upon as needed, but when called upon, thesnapshots produce only the same data or data display that was originallycaptured. Snapshots cannot readily be altered or re-configured asdesired, except for adding text and graphics to help understand the dataand reports that have been captured, but not for changing the data orthe reports. Additionally, there are often specific configurationrequirements, pre-conditions, and pre-configurations which must metbefore a snapshot can be taken. These and other limitations andrestrictions are known to those skilled in the database art.

SUMMARY

Provided herein are methods and systems for data management andanalysis. The methods and systems described, in one aspect, canfacilitate the analysis of information to provide usable output forvarious users of a database.

In an aspect, provided are methods for analyzing information within adatabase that comprises one or more database structures whichcollectively contain a plurality of data records, with each recordhaving at least two data element types, and with at least one of thedata element types having a different data element value from the dataelement value for the corresponding data element type in at least oneother record in the database; the method characterized by the steps ofreading the plurality of records, instantiating an initial datastructure for each unique data element type within the plurality ofrecords, creating an entry in the initial data structure for each dataelement type for each unique data element value within that data elementtype, selecting one or more database structures within the database,instantiating a final data structure for the selected databasestructures in which the data element value for each data element typereflects the entry made in the initial data structures for that dataelement value.

In another aspect, provided are methods comprising receiving a query forinformation from the database, determining particular data element typesand data element values that are the subject of the query, instantiatinga query data structure containing the data element types and the dataelement values that are the subject of the query, identifying recordswithin the database that contain one or more data element types and/ordata element values that are included in the query data structure, andinstantiating a results data structure comprising information relatingto the identified records.

In a further aspect, provided are methods for analyzing information,comprising identifying, in a database, unique data element types,generating a plurality of initial data structures corresponding to theunique data element types, wherein the plurality of initial datastructures comprise unique data elements associated with thecorresponding unique data element type, generating a final datastructure based on the plurality of initial data structures, wherein thefinal data structure is a non-redundant combination of the plurality ofinitial data structures, and providing search results relevant to asearch query based on the final data structure.

In another aspect, provided are methods, comprising receiving a queryfor information from a database, identifying data element types relevantto the query and data element values relevant to the query, generating aquery structure based on the identified data element types and dataelement values, identifying a data structure relevant to the querystructure, wherein the data structure comprises non-redundant dataelement values from the database, identifying records in the datastructure based on the query structure, and providing the records inresponse to the query for information.

It is an object of one or more embodiments of the present disclosure toprovide a means for easily capturing dynamic presentations orvisualizations of data that are determined to be beneficial. It is anobject of one or more embodiments of the present disclosure to provide ameans for affording easy the communication and use of such captureddynamic presentations or visualizations. It is an object of one or moreembodiments of the present disclosure to provide a means for affordingeasy access to, and modification of, previously captured dynamicpresentations or visualizations. It is an object of one or moreembodiments of the present disclosure to provide a means for affordingalternative analysis of such captured presentations or visualizationwhile preserving or curating the underlying data that comprises thecaptured presentations or visualizations. It is an object of one or moreembodiments of the present disclosure to provide a means for easilyupdating all or a portion of the data contained within the capturedpresentations or visualizations without changing the specifications forthe captured presentations or visualizations.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims. It is to be understood that both theforegoing general description and the following detailed description areexemplary and explanatory only and are not restrictive or limiting, aspresented herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is an exemplary computing system;

FIG. 2a illustrates an exemplary database and visualizations thereof;

FIG. 2b illustrates an exemplary database and visualizations thereof;

FIG. 2c illustrates an exemplary database and visualizations thereof;

FIG. 3a illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 3b illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 3c illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 4 illustrates various intermediate data structures for anembodiment of the present disclosure;

FIG. 5 illustrates a final data structure for an embodiment of thepresent disclosure; and

FIG. 6 illustrates a visualization of the final data structure for anembodiment of the present disclosure;

FIG. 7 illustrates a data display as a result of a counting operation;

FIG. 8 illustrates another data display as a result of a countingoperation;

FIG. 9 illustrates selections in a data display;

FIG. 10a illustrates an exemplary auto-complete operation;

FIG. 10b illustrates another exemplary auto-complete operation;

FIG. 11 illustrates yet another exemplary auto-complete operation;

FIG. 12a illustrates a further auto-complete operation;

FIG. 12b illustrates a modification of the auto-complete operation ofFIG. 12 a;

FIG. 12c illustrates entry of search terms for a specific number offield combinations;

FIG. 13a illustrates the steps of a method according to one embodimentof the present disclosure;

FIG. 13b illustrates the steps of a method according to one embodimentof the present disclosure;

FIG. 14 illustrates the steps of a method according to anotherembodiment of the present disclosure;

FIG. 15 illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 16 illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 17 illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 18 illustrates the steps of a method according to one embodiment ofthe present disclosure;

FIG. 19 illustrates the steps of a method according to one embodiment ofthe present disclosure; and

FIG. 20 illustrates the steps of a method according to one embodiment ofthe present disclosure.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described inmore detail, it is to be understood that the methods and systems are notlimited to specific steps, processes, components, or structuredescribed, or to the order or particular combination of such steps orcomponents as described. It is also to be understood that theterminology used herein is for the purpose of describing exemplaryembodiments only and is not intended to be restrictive or limiting.

As used herein the singular forms “a,” “an,” and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise. Values expressed as approximations, by use of antecedentssuch as “about” or “approximately,” shall include reasonable variationsfrom the referenced values. If such approximate values are included withranges, not only are the endpoints considered approximations, themagnitude of the range shall also be considered an approximation. Listsare to be considered exemplary and not restricted or limited to theelements comprising the list or to the order in which the elements havebeen listed unless the context clearly dictates otherwise.

Throughout the specification and claims of this disclosure, thefollowing words have the meaning that is set forth: “Comprise” andvariations of the word, such as “comprising” and “comprises,” meanincluding but not limited to, and are not intended to exclude, forexample, other additives, components, integers or steps. “Exemplary”means “an example of”, but not essential, necessary, or restricted orlimited to, nor does it convey an indication of a preferred or idealembodiment. “Include” and variations of the word, such as “including”are not intended to mean something that is restricted or limited to whatis indicated as being included, or to exclude what is not indicated.“May” means something that is permissive but not restrictive orlimiting. “Optional” or “optionally” means something that may or may notbe included without changing the result or what is being described.“Prefer” and variations of the word such as “preferred” or “preferably”mean something that is exemplary and more ideal, but not required. “Suchas” means something that is exemplary.

Steps and components described herein as being used to perform thedisclosed methods and construct the disclosed systems are exemplaryunless the context clearly dictates otherwise. It is to be understoodthat when combinations, subsets, interactions, groups, etc. of thesesteps and components are disclosed, that while specific reference ofeach various individual and collective combinations and permutation ofthese may not be explicitly disclosed, each is specifically contemplatedand described herein, for all methods and systems. This applies to allaspects of this application including, but not limited to, steps indisclosed methods and/or the components disclosed in the systems. Thus,if there are a variety of additional steps that can be performed orcomponents that can be added, it is understood that each of theseadditional steps can be performed and components added with any specificembodiment or combination of embodiments of the disclosed systems andmethods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the Examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices,whether internal, networked or cloud based.

Embodiments of the methods and systems are described below withreference to diagrams, flowcharts and other illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions

FIG. 1 is a block diagram illustrating an exemplary operatingenvironment for performing the disclosed methods. This exemplaryoperating environment is only an example of an operating environment andis not intended to suggest any limitation as to the scope of use orfunctionality of operating environment architecture. Neither should theoperating environment be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, structures, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 101 such as is illustrated inFIG. 1. The components of the computer 101 can comprise, but are notlimited to, one or more processors or processing units 103, a systemmemory 112, and a system bus 113 that couples various system componentsincluding the processor 103 to the system memory 112. In the case ofmultiple processing units 103, the system can utilize parallelcomputing.

The system bus 113 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, sucharchitectures can comprise an Industry Standard Architecture (ISA) bus,a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, an AcceleratedGraphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI),a PCI-Express bus, a Personal Computer Memory Card Industry Association(PCMCIA), Universal Serial Bus (USB) and the like. The bus 113, and allbuses specified in this description can also be implemented over a wiredor wireless network connection and each of the subsystems, including theprocessor 103, a mass storage device 104, an operating system 105,management software 106, management data 107, a network adapter 108,system memory 112, an Input/Output Interface 110, a display adapter 109,a display device 111, and a human machine interface 102, can becontained within one or more remote computing devices 114 a,b,c atphysically separate locations, connected through buses of this form, ineffect implementing a fully distributed system.

The computer 101 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 101 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 112 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 112 typically contains data such as management data 107and/or program modules such as operating system 105 and managementsoftware 106 that are immediately accessible to and/or are presentlyoperated on by the processing unit 103.

In another aspect, the computer 101 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 11 illustrates a mass storage device 104 whichcan provide non-volatile storage of computer code, computer readableinstructions, data structures, program modules, and other data for thecomputer 101. For example and not meant to be limiting, a mass storagedevice 104 can be a hard disk, a removable magnetic disk, a removableoptical disk, magnetic cassettes or other magnetic storage devices,flash memory cards, CD-ROM, digital versatile disks (DVD) or otheroptical storage, random access memories (RAM), read only memories (ROM),electrically erasable programmable read-only memory (EEPROM), and thelike.

Optionally, any number of program modules can be stored on the massstorage device 104, including by way of example, an operating system 105and management software 106. Each of the operating system 105 andmanagement software 106 (or some combination thereof) can compriseelements of the programming and the management software 106. Managementdata 107 can also be stored on the mass storage device 104. Managementdata 107 can be stored in any of one or more databases known in the art.Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft®SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases canbe centralized or distributed across multiple systems.

In another aspect, the user can enter commands and information into thecomputer 101 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like These andother input devices can be connected to the processing unit 103 via ahuman machine interface 102 that is coupled to the system bus 113, butcan be connected by other interface and bus structures, such as aparallel port, game port, an IEEE 1394 Port (also known as a Firewireport), a serial port, or a universal serial bus (USB).

In yet another aspect, a display device 111 can also be connected to thesystem bus 113 via an interface, such as a display adapter 109. It iscontemplated that the computer 101 can have more than one displayadapter 109 and the computer 101 can have more than one display device111. For example, a display device can be a monitor, an LCD (LiquidCrystal Display), or a projector. In addition to the display device 111,other output peripheral devices can comprise components such as speakers(not shown) and a printer (not shown) which can be connected to thecomputer 101 via Input/Output Interface 110. Any step and/or result ofthe methods can be output in any form to an output device. Such outputcan be any form of visual representation, including, but not limited to,textual, graphical, animation, audio, tactile, and the like.

The computer 101 can operate in a networked environment using logicalconnections to one or more remote computing devices 114 a,b,c. By way ofexample, a remote computing device can be a personal computer, portablecomputer, a server, a router, a network computer, a peer device or othercommon network node, and so on. Logical connections between the computer101 and a remote computing device 114 a,b,c can be made via a local areanetwork (LAN) and a general wide area network (WAN). Such networkconnections can be through a network adapter 108. A network adapter 108can be implemented in both wired and wireless environments. Suchnetworking environments are conventional and commonplace in offices,enterprise-wide computer networks, intranets, and the Internet 115.

For purposes of illustration, application programs and other executableprogram components such as the operating system 105 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 101, and are executed by the data processor(s)of the computer. An implementation of management software 106 can bestored on or transmitted across some form of computer readable media.Any of the disclosed methods can be performed by computer readableinstructions embodied on computer readable media. Computer readablemedia can be any available media that can be accessed by a computer. Byway of example and not meant to be limiting, computer readable media cancomprise “computer storage media” and “communications media.” “Computerstorage media” comprise volatile and non-volatile, removable andnon-removable media implemented in any methods or technology for storageof information such as computer readable instructions, data structures,program modules, or other data. Exemplary computer storage mediacomprises, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by acomputer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

An example of a database 200 is illustrated in FIG. 2a . This databasecomprises a single database structure, e.g., a table, containing aplurality of records having multiple data elements. Each of the dataelements has a data element type and a data element value (for example“Make” is the data element type and “Honda” is the data element value).The database can comprise at least some records in which the dataelement values are different from those in other records and some inwhich the values are the same or “null”. Although FIG. 2a illustrates asingle table, the multiple records may be stored in other databasestructures such as data cubes, data arrays, data strings, flat files,lists, vectors, and so forth; and the number of database structures maybe greater than just one and may consist of multiple types andcombinations of database structures. While these and other databasestructures can be used with, and as part of, the methods and systemsdisclosed, the remaining description will refer to tables, vectors,strings and data cubes solely for convenience. Additional databasestructures can be included within the database illustrated as an exampleherein, with such structures including additional information pertinentto the database such as, in the case of vehicles for example; color,optional packages, etc. Each table can comprise a header row 201 whichcan identify the various data element types, often referred to as thedimensions or the fields, that are included within the table. Each tablecan also have one or more additional rows 202 which comprise the variousrecords making up the table. Each of the rows would contain data elementvalues 203 (including null) for the various data element typescomprising the record.

Related data element types may exist among the tables, for example thedatabase may contain two or more tables, each having “Transmission” as adata element type. This can be done to place top level information in asingle table and then to use common data element fields to link that toplevel table to other dependent tables to provide more detailedinformation in these dependent tables. The common or related dataelement types can serve as keys to link or associate the tables andthereby provide further detail regarding the subject matter of the dataelement types. For example, with the data element type “Transmission”there may be a dependent table for the data element value “Manual,”indicating additional information like “4-Speed” or “5-Speed.” Althoughthe use of common data element types can provide automatic linkage,techniques can be used to specify a linkage, in which event the use ofcommon data element types is not required.

Techniques for performing an analysis to determine the relationshipsbetween the various tables, and to virtually connect tables that aredependent through their linkage can be used. If two tables have morethan one variable in common a “loop” is created, and techniques can beused to resolve such loops and thereby simplify the dependencies. Inthis manner the relationships between the various data element typescomprising the database can be determined. It should be noted that, inaddition to relationships between the various tables comprising thedatabase, there is also an implicit link or association between each ofthe data element types comprising a single row or record within a table.

Once all the dependencies between the various tables or other databasestructures are known, it is possible to display, in a simplified format,the relationships among the various data element types and data elementvalues included within the database, for example, to create list boxesor other data display objects to list the unique data element values foreach relevant data element type. To assist in this process, conversionstructures can be used to resolve dependencies, for example, to add themore specific entries, such as 4-speed or 5-speed, for “Manual” to thedata element type “Transmission.” An example of list boxes displayingthe unique data element values for selected fields or data element typesin the database (in which the dependencies have been removed) isillustrated in FIG. 2 b.

The database can be queried by specifying the data element types anddata element values of interest and by further specifying any functionsto apply to the data contained within the specified data element typesof the database. The functions which can be used within a query caninclude, for example, expressions using statistics, sub-queries,filters, mathematical formulas, and the like, to help the user to locateand/or calculate the specific information wanted from the database. Oncelocated and/or calculated, the results of a query can be displayed tothe user with various visualization techniques and objects such as thelist boxes illustrated in FIG. 2 c.

The result of a standard query is typically a smaller subset of the datawithin the database, or a result set, which is comprised of the records,and more specifically, the data element types and data element valueswithin those records, along with any calculated functions, that matchthe specified query. For instance, as indicated in FIG. 2c , the dataelement value “Coupe” can be specified as a query or filtering criteria(this is indicated by the highlighting in FIG. 2c ) and the resultingdata element values that are displayed in the list boxes for theincluded data element types are now only those which apply to the dataelement value “Coupe.” Referring back to FIG. 2a , note that there areonly 5 records with a data element value of “Coupe,” and that the queryhas eliminated all records not having that data element value. This isevidenced by comparing FIG. 2b to FIG. 2c . The present methods andsystems overcome these limitations of the current database technology.

In an aspect of the present disclosure, illustrated in FIG. 3a , one ormore of the records in one or more of the data structures comprising thedatabase can be read at 301 a, for example, by using a SELECT statementwhich selects applicable database tables and the selected records.Typically the records can be read into the system memory 112 of thecomputer 101, although the records can also be read into external memory(for example, cloud storage). Accordingly, for one or more tables in thedatabase, a computer can carry out one or more of the following steps inany order. The field names, e.g. the data element types 201, of thetable can be successively read. In an aspect, when a new data elementtype is encountered, an initial data structure can be instantiated(e.g., created, displayed, etc. . . . ) for the new data element type at302 a. Data records (or remaining rows of the tables) (such as records202) can be read and such data element values (such as data elementvalues 203) from the records can be entered (e.g., populated, etc. . . .) into the applicable initial data structure for the corresponding dataelement type at 303 a. In an aspect, for each data element value, thedata structure of the corresponding data element type can be checked toestablish if that value has previously been entered. If so, it will notbe re-entered, such that the initial data structure for each dataelement type will only contain the unique data element values for thatdata element type, but will not repeat non-unique data element values.The result of performing these steps affords a display of the resultantdata such as that illustrated in FIG. 4. Each of these initial datastructures can then be associated with a unique identifier (such as ahash function) and the resulting information can be stored in memory.

In a further aspect, at 304 a, one or more of the unique data elementtypes and one or more of the unique data element values within a dataelement type can be assigned a code (for example, a binary code) thatcan be stored in the computer memory and easily processed by thecomputer, and that can be used instead of the actual alpha-numericvalues for the data element values when processing the database. Forexample, for each data element value of each data element type, themethods and systems can assign a binary code, using the same binary codefor each data element value which is the same, and a different binarycode for each data element which is different. For each unique dataelement value, the methods and systems can create an entry that includesthe assigned binary codes in the initial data structure for that dataelement type.

However, the methods and systems do not require binary coding. The codewhich is assigned can be a code that can also be sorted such as anumeric or alphabetic code. The ability to sort the values can enabledisplaying the values as part of a visualization of the data or whenperforming various functions like identifying minimum and maximumvalues. The assignment of a binary code can be performed when datarecords are first read from the database. Accordingly, the assignedbinary code for each unique data element value and each unique dataelement type can be inserted in the corresponding initial data structurefor that data element type and for the associated data element valuesfor that data element type. If the data element type or the data elementvalue is new it can be assigned a new binary code (for example, the nextbinary code in ascending order) before being inserted in the datastructure. In other words, for each unique data element type, a uniquebinary code can be assigned to that data element type and to each uniquedata element value associated with that data element type. FIG. 4illustrates exemplary initial data structures that can be instantiatedfor various data element types along with the exemplary binary codesthat can be been assigned to different data element values and the dataelement types that are included in the database of FIG. 2. For ease ofunderstanding, alpha-numeric headers have been added to the variousinitial data structures.

Reference is now made to U.S. Pat. No. 8,244,741 B2, which is assignedto the same Assignee as the present application, and the teachings ofwhich are incorporated herein by reference, wherein the process ofassociating a hash function with the relevant data structures andstoring in memory the hash function along with the applicable databaseinformation contained in the structures, is described.

In an embodiment of the present disclosure, the methods and systemsprovided can instantiate a data structure, as illustrated in FIG. 4(Table 7), that contains the unique data element types within thesubject database. Such initial data structure facilitates locating andutilizing other initial data structures (Tables 1-6) for each of theunique data element types. This initial data structure can also beassociated with a unique identifier (such as a hash function) and theresulting information stored in memory. As used herein, “initial” doesnot require that the initial data structures be the first datastructures created as part of the methods and systems disclosed. Otherdata structures can be created prior to, and after, the initial datastructures.

Returning to FIG. 3a , at step 305 a, the methods and systems candetermine if the database contains one or multiple database structuresand if so, determine, identify, and resolve the dependencies between thedatabase structures. In an aspect, the methods and systems caninstantiate one or more final data structures that can fully representthe database at 306 a, with all dependencies removed; and again thesefinal data structure(s) can be associated with a unique databaseidentifier or hash function, and the resultant information stored inmemory. In an aspect, the methods and systems can instantiate a finaldata structure in which the data element values for each data record inthat database object are replaced by the assigned binary code for thatdata element value. In a further aspect, at step 306 a, for one or moredatabase objects which are dependent, the methods and systems can createa conversion structure that can resolve such dependencies using theassigned binary codes for the data element values, and can use suchconversion structures to create a final data structure for suchdependent database objects. As used, herein, “final” does not indicatethat further processing is foreclosed, or that that the final datastructure is the last data structure created.

Using Tables 1-7 it is possible to create the referenced final datastructure(s) which reflect various records contained within thedatabase. Table 8 of FIG. 5 illustrates such a final data structure inwhich the assigned binary codes for the various data element values havebeen substituted for the actual alpha-numeric values. In addition tofacilitating the processing of the database, Tables 1-7 can also be usedas “look-up” tables to convert between the actual alpha-numeric valuesof the data element values and the assigned binary values correspondingto their alpha-numeric counterpart.

It is one aspect of the present disclosure that the above processes needonly be completed once each time a database is loaded or reloaded, andthe appropriate interim and final data structures can be created andstored in memory along with their associated identifier. These processesneed not be repeated when queries are entered or changed, or whenvisualizations are entered or changed. However, the processes can becompleted at any point in the life of a database.

In an aspect, illustrated in FIG. 3b , provided are methods and systemsfor query handling. If a query is made to analyze and/or interpret datawithin the database, the information stored through the above processes,as illustrated in Tables 1-8 can be used to process both the dataelement types and data element values that are the subject of the query.Upon receiving a query to the database, the methods and systems candetermine the particular data element types and functions that areapplicable to the query as well as the data element types that areapplicable to the functions and can initiate a query data structurecontaining this information at 301 b.

To facilitate this processing the data element types and data elementvalues of the query, the query itself can be converted to use theassigned binary codes as determined in step 304 a of FIG. 3a . Forexample, if it is desired to find within this database all vehicleshaving the Type—“Coupe,” and the Make—“Honda.” The binary code for Coupeis “0” and the binary code for Honda is “3.” These values, along withany functions required to be calculated as part of the query, can bestored in a query data structure such as a vector, data string, dataarray, table, and the like, that can be processed with the final datastructure, in this case, Table 8 of FIG. 5, to execute the query andyield the query results. It should also be noted that if the querycriteria involve functions which require calculations, an initial datastructure can be instantiated for each of the functions which can befilled with the results of the functions as they are calculated. Again,the results of a query can be inserted and associated with a uniquebinary code assigned in the same manner as used with data elementvalues. In an aspect, the data structures comprising the query can alsobe associated with a unique identifier (e.g., a hash function) and theresulting information stored in memory. This process can occur for eachnew query (including modifications of an existing query.) Thus, givenadequate memory, all queries that have been made to the database can bestored, and such queries can be reutilized without the need forsignificant incremental processing. It should be noted that by using theinstantiated data structures and the assigned binary codes instead ofthe actual alpha-numeric values, the required memory and processing timeare substantially reduced.

The results obtained from a query, including the results of calculatedfunctions specified within the query, can be included in a datastructure associated with the data structure for that query. Theresulting information can be stored in memory along with an associatedunique identifier that can be used to retrieve the already processedresult set whenever the query that yielded that result set needs to bere-executed. This results in a substantial savings in time andprocessing.

The data element types that are the subject of query can be referred toas classification element types and the data element types that are thesubject of a function of filtering criteria can be referred to asfunction element types. These classification and function element typescan be used to select a final data structure that contains the greatestnumber of these included element types at 302 b. The final datastructure selected can be referred to as a starting table. In theillustrated database there is only one final data structure and so thisselection is simple. Databases with multiple final data structures canbe processed in the same manner. If there are multiple final datastructures having comparable element types, the starting table can beselected by using the final data structure having the most records.

The starting table can be processed using the query data structure toidentify records in the database containing the data element types andthe data element values which are the subject matter of the query at 303b. In an aspect, the methods and systems can identify records thatcontain data element values that are included within the query datastructure. The methods and systems can initiate a result data structureand store within the result data structure information relating to theidentified records at 304 b. The result data structure can beinstantiated to store the results of the processing, which can includeresults data structure information relating to the identified records.This information can be stored using the assigned identifier. Suchinformation can include the applicable record number(s), the dataelement types and data element values, and the calculated functionsmatching elements of the query (or query data structure) as well as therecords, data element types and data element values, and the calculatedfunctions that match less than all or none of the elements of the query.

Reference is now made to U.S. Pat. No. 6,236,986 B1 and hereinafter “the'986 Patent” which is assigned to the same Assignee as the currentapplication, and the teachings of which are incorporated herein byreference. This patent teaches the use of selection, status andfrequency data strings, arrays, vectors and other data structures. Forsimplicity, future references to these structures will use the termsvector, strings and cubes even though other data structures should alsobe considered as being usable with the present methods and systems.Utilizing the data structures referenced above (including the sort orderfor the referenced data element values therein) selection, status andfrequency vectors can be instantiated for the various data elementtypes, data element values and calculated results.

In an aspect, the vectors described in the '986 Patent for the variousdata element types can be a numeric string having one position for eachunique data element value of that data element type. Accordingly, suchstring will have a total number of positions equal to the number ofunique data element values in that data element type. Each of the uniquedata element values for a data element type can be assigned a uniquevalue that represents that particular data element value for that dataelement type. For example, if there are eight unique data element valuesfor a particular data element type, each data element value can beassigned a number between “0” and “7,” or “1” and “8,” or “000” and“111,” etc., depending upon the convention or system utilized, and anyconvention or system can be used according to the teachings of thepresent disclosure. It is possible to process (through Booleancalculations and otherwise) these vectors regardless of the numberingsystem or convention used, and conversions can be made between thevarious systems and conventions. In the case of the frequency vector,which is typically a counting vector, the values which are assigned willreflect the number of occurrences of the particular data element valueassociated with that vector. It should be noted that differentembodiments of the present methods and systems can use differentcombinations of the vectors described in the '986 Patent or can use themfor different purposes as will be described below.

As illustrated in Table 8a, below, an aspect of the present methods andsystems can instantiate an initial selection vector for each unique dataelement type in the data structures, with a number of positions equal tothe number of unique data element values for that data element type, anda single bit in each position, having a value equal to “0”, indicatingthat no query has been entered or received, and no selections have beenmade for that data element type. Similarly the initial status vector canbe instantiated for each data element type, which can also have a numberof positions equal to the number of unique data element values, andsingle bit with a value equal to “1” in all positions, indicating thatall selections are possible. The frequency vector can also beinstantiated, having a like number of positions, which can all initiallybe set to “0” values since no queries have been processed and no resultshave been counted. This information can be used to easily present thedata to the user that is contained within the final data structures, forexample, using list boxes such as those illustrated in FIG. 6, with alldata element values for all data element types being displayed since, asreflected by the vectors displayed in Table 8a, no queries or selectionshave been made, resulting in the initial status vectors having a “1” inall positions and the initial frequency and selection vectors having a“0” in all positions.

TABLE 8a Initial Selection, Status and Frequency Vectors Vectors TypeEngine Transmission Price Make Model Selection 0, 0, 0 0, 0, 0, 0, 0, 0,0, 0 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0, 0 0, 0,0, 0 0, 0, 0, 0, 0, 0 Status 1, 1, 1 1, 1, 1, 1, 1, 1, 1, 1 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1 1, 1, 1, 1, 1, 1, 1, 1, 1 1, 1, 1, 1 1, 1, 1, 1,1, 1 Frequency 0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0 0, 0, 0, 0, 0, 0

When a query is made, for example, for all vehicles having theType—“Coupe,” the above selection vectors can be updated to reflect thequery. The selection vector for the “Type” data element type becomes1,0,0; indicating that “Coupe” has been selected (and Hatchback andSedan were not selected). This is the same as entering “True” for thedata element value “Coupe” and a “False” for all other possible elementvalues for that data element type. It should be understood thatselections can be made in more than one field or data element type, andif this is the case the selection vector for that data element typewould be updated to include a “1” for the selected data element value,and a “0” for the rest of the data element values. Thus if “Honda” isalso selected, the resulting selection vector for the data element type“Make” will be 0,0,0,1,0. Similarly more than one data element value canbe selected within a data element type, and if this is the case then theselected values will still be represented by a “1” and the non-selectedvalues will be represented by a “0.” The updated selection vectorsassociated with the selection of “Coupe” and “Honda” are illustrated inTable 8b. It should also be understood that the selection vectors wouldbe updated and processed with every new selection or modification of aselection. With respect to the current example in which two selectionshave been made, there will be two updates to the selection vector andthat vector will be processed two times as further described below.

The updated selection vector can then used to update the status vector,accordingly with this embodiment, the status vector can be updated toreflect the selection of both “Coupe” and “Honda”. Basically the updatedselection vector can be copied into the status vector to create theupdated status vector for each data element type. The updated statusvector reflecting the selection of Coupe and Honda is illustrated inTable 8b.

TABLE 8b Selection, Status and Frequency Vectors for the various dataelement types after receipt of Query selecting both Coupe and HondaVectors Type Engine Transmission Price Make Model Selection 1, 0, 0 0,0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 0, 0, 0, 0, 0, 0,0, 0, 0 0, 0, 0, 0 0, 0, 0, 0, 0, 0 Status 1, 0, 0 0, 0, 0, 0, 0, 0, 0,0 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 0, 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0,0 0, 0, 0, 0, 0, 0 Frequency 0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0 0, 0, 0, 0,0, 0

Using the updated selection vector resulting from each modificationthereof, the final data structures can be processed and an initialstatus vector for each such final data structure can be instantiated,starting with the designated starting table and progressing through anyother final data structures containing the data element type affected bythe query. The initial status vector for each final data structure canhave a number of positions equal to the number of records in the datastructure. So in the case of the present example this vector will have16 positions, one for each of the 16 records contained in the startingtable, Table 8. In an embodiment of the present disclosure, this initialtable status vector can have a single bit in each position that will beused to indicate whether the corresponding record includes a selecteddata element value as contained the selection vector. The selectionvector can be compared with the various records comprising the finaldata structures, in this case the starting Table 8, which comparisonwould result in an initial status vector for the table with a “1” ineach position for records which have a data element value in theaffected data element type that matches the data element value for suchdata element type in the selection vector. Note that in the presentexample, the values in each column of Table 8a are numbers from “0” to“n” where “n” is equal to the number of unique data element values forthat data element type minus one (since the methods and systems startedwith the first listed value equal to “0”) while the selection vectorsare in the format of a Boolean string with a “1” in the position of theselected data element value. Even though the format of the numbers aredifferent, the comparison can still be made.

In the present example, the initial status vector for Table 8a will havea “1” in the positions corresponding to the first five records, and a“0” in all the other positions, indicating that only the first fiverecords have “Coupe” indicated for the data element type, “Type.”Similarly after processing the updated selection vector due to thefurther selection of “Honda,” the updated status vector for the tablewill indicate that only records one through three match both of thesequery criteria, and record 13 matches Honda but not Coupe. The updatedstatus vector for Table 8 is set forth Table 8c.

TABLE 8c Initial table status vector After selection of Coupe: 1, 1, 1,1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 After selection of Honda 1, 1, 1,0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0

In addition to instantiating the initial data structure for the tablesor other final data structures, the status vectors for each data elementtype can also be updated with each update of the status vector for eachfinal data structure. Since the initial status vector for Table 8 afterthe selection of “Coupe” indicated that only records one through fivehad “Coupe” as a value for the data element type “Type,” only thesematching records need be examined for the other data element typescomprising each record. As each of the first five records is examined,the status vector for each final element type can be updated to reflectthe presence of the particular data element value for that data elementtype. Accordingly the vector for the data element type “Type” willremain “1,0,0” since all five records have the data element value“Coupe.” The vector for the data element type “Engine” will have a valueof “1” for the position corresponding to the data element value “1.8”and “0's” for the other positions. The vector will not change afterprocessing the second record since it also includes a “1.8.” Afterprocessing the third record this vector will change to include a 1 inthe 4th position since this record includes a data element value of“2.4.” The remaining vectors can be updated in the same manner.Accordingly, the updated status vectors for each of the data elementtypes after processing the selection vector reflected the selection of“Coupe” is set forth in Table 8d.

TABLE 8d Field Element Type Status Vectors reflecting the selection ofCoupe Record Type Engine Transmission Price Make Model 1 1, 0, 0 0, 1,0, 0, 0, 0, 1, 0 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0 0, 0, 0, 1, 0, 0, 0, 0,0 0, 0, 0, 0, 0 0, 0, 0, 0, 0 2 1, 0, 0 0, 1, 0, 0, 0, 0, 1, 1 0, 0, 1,1, 0, 0, 0, 0, 0, 1, 0 0, 0, 0, 1, 0, 0, 0, 0, 0 0, 0, 0, 0, 0 0, 0, 0,0, 0 3 1, 0, 0 0, 1, 0, 1, 0, 0, 1, 1 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0 1,0, 0, 1, 0, 0, 0, 0, 0 1, 0, 0, 0, 0 0, 0, 0, 0, 0 4 1, 0, 0 0, 1, 0, 1,0, 0, 1, 1 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0 1, 0, 1, 1, 0, 0, 0, 1, 0 1,0, 0, 0, 0 0, 0, 0, 0, 0 5 1, 0, 0 0, 1, 0, 1, 0, 0, 1, 1 0, 0, 1, 1, 0,1, 0, 1, 1, 1, 0 1, 0, 1, 1, 0, 0, 0, 1, 1 1, 0, 0, 0, 0 0, 0, 1, 0, 0

After the selection of “Honda” the selection vector will be updated asillustrated in Table 8c, however, the status vectors for the fieldelement types need not be updated since the first three records havealready been processed and the values have not changed.

As the status vector for each data element type for each record isupdated, the frequency vector for that data element type can also beupdated. The frequency vector for each data element type reflects acount of the various data element values in that data element type.Accordingly with each occurrence of a data element value within therecords the position of the frequency vector is incremented by one. Theupdated frequency vectors for records one through five, reflecting theselection of “Coupe” are illustrated in Table 8e. This process can berepeated for further selections, such as the selection of Honda in thepresent example.

TABLE 8e Field Element Type Frequency Vectors reflecting the selectionof Coupe Record Type Engine Transmission Price Make Model 1 1, 0, 0 0,1, 0, 0, 0, 0, 1, 0 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0 0, 0, 0, 1, 0, 0,0, 0, 0 0, 0, 0, 0 0, 0, 0, 0, 0 2 2, 0, 0 0, 2, 0, 0, 0, 0, 1, 1 0, 0,1, 1, 0, 0, 0, 0, 0, 0, 2, 0 0, 0, 0, 2, 0, 0, 0, 0, 0 0, 0, 0, 0 0, 0,0, 0, 0 3 3, 0, 0 0, 2, 0, 1, 0, 0, 1, 2 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,3, 0 1, 0, 0, 2, 0, 0, 0, 0, 0 0, 0, 0, 0 0, 0, 0, 0, 0 4 4, 0, 0 0, 2,0, 1, 0, 0, 2, 2 0, 0, 1, 1, 0, 0, 2, 0, 1, 0, 3, 0 1, 0, 1, 2, 0, 0, 0,1, 0 0, 0, 0, 0 0, 0, 0, 0, 0 5 5, 0, 0 0, 2, 0, 1, 0, 0, 2, 3 0, 0, 1,1, 0, 1, 2, 0, 1, 1, 3, 0 1, 0, 1, 2, 0, 0, 0, 1, 1 0, 0, 0, 0 0, 0, 1,0, 0

In another embodiment of the present methods and systems, the statusvectors for the final data structures and for the data element types canhave a number of bits in each position that is equal to or greater thanthe number of data element types in which selections have been made inthe query. Thus as selections are added, another bit can be added toeach position of these vectors. Accordingly, in the present example inwhich two selections have been made in two data element types, eachposition within these vectors will have at least two bits. Also withinthese vectors each position will contain additional informationreflecting the order of the selections. Each such data element type istherefore assigned a unique and subsequent position within the vectorsto indicate an association with the selections in that data elementtype. In this example “Type” is assigned the first position and “Make”assigned the second.

In this embodiment, the initial status vector for each data element typeis cleared (all positions set to 0). If the corresponding data elementvalue exists in the selection vector, the position in the status vectorcorresponding to the data element type is set to 1. This process isrepeated for the “Make” data element type. Accordingly the status vectorafter the receipt of a query selecting “Coupe” and “Honda” isillustrated in Table 8f. Note that within this vector “Coupe” isindicated as the first selection with the value “01,” and “Honda” isindicated as the second selection with the value “10.”

TABLE 8f Updated Status Vector for the various data element types afterreceipt of Query selecting both Coupe and Honda Vectors Type EngineTransmission Price Make Model Status 01, 00, 00 00, 00, 00, 00, 00, 0000, 00, 00, 00, 00, 00, 00, 00, 00, 00, 00, 00, 00, 00, 00, 00 00, 00,00, 00, 10, 00 00, 00, 00, 00, 00, 00, 00, 00 00, 00

As before, an initial status vector can be created for the table. Thistable status vector can be used for the storage of intermediatecombinations of associations. In an aspect, for each data record one ormore of the following steps are taken. First, for each data elementvalue in the record, the Boolean string corresponding to that dataelement value in the status vector for each data element type can befound. Next these strings can be combined by applying a logicalinclusive OR operation. Next, the Boolean string resulting from thisoperation can be stored at the position in the table status vectorcorresponding to the current data record. This process can be repeatedfor each record in the final data structures. The resulting statusvector for Table 8 is illustrated in Table 8g.

TABLE 8g Initial table status vector After selection of Coupe and 11,11, 11, 01, 01, 0, 0, 0, 0, 0, 0, 0, 10, Honda: 0, 0, 0

Once the table status vector for the final table(s) has been determined,it can be associated with a unique identifier (typically a hashfunction) and this information, reflecting the data element value foreach data entry type for each record in the final data structure(s), canbe stored in memory. A final status vector for each data element typereflecting the results of the query can then be computed based upon thetable's initial status vector. For each element of each data elementtype's status vector, the subset of records in the table that contain adata element value corresponding to the element currently being computedcan be examined. From this subset the largest of all of the elements inthe table status vector that correspond to records in the subset beingexamined can be selected. The definition of largest in this context cancomprise, for example, imposing a strict partial order on the set ofcombinations of associated data element types as represented by Booleanstrings. The criteria used to impose such an order include, but are notlimited to, the number of associated data element types, whether aparticular field is present, or by assigning weights to the presence ofeach field and using the weight to determine order. The vectors in Table8h represent the resulting status vector after processing the query forrecords matching the data element values “Coupe” and “Honda.” Withinthese vectors note that the value “11” indicates that the representeddata element value has matched both query criteria, the value “01”indicates that the represented data element value has matched only thefirst criterion, and the value “10” indicates that the represented dataelement value has match only the second criterion.

The status vector therefore indicates that the data element “Coupe” asone of the selection criteria matched both selections. The data elementvalue “Sedan,” however, while not matching the first selection did matchthe second selection. Accordingly at least one record matched “Honda”while not matching “Coupe” (rather it matched “Sedan” as denoted by the“10” entry in the “Type” Data Element Type). The frequency vectorillustrated in Table 8h indicates that there were three occurrences ofthe data element value “Coupe” within the matching records. Similarlythere were two occurrences of the data element value “1.8” within thematching records. Note that, as with the first embodiment with the twoselections, only the first three records are of interest.

TABLE 8h Final Status and Frequency Vectors after traversing all recordsin Table 8 Vector Type Engine Transmission Price Make Model Status 11,00, 10 00, 11, 00, 11, 11, 11 00, 00, 11, 11, 00, 01, 01, 11, 00, 01,11, 00, 00, 01, 01 00, 01, 11, 00, 11, 00 00, 00, 00, 00, 00, 00, 00 00,01, 00, 00, 00 Frequency 3, 0, 0 0, 2, 0, 1, 0, 1, 2 0, 0, 1, 1, 0, 0,0, 0, 0, 3, 1, 0, 0, 2, 0, 0, 0, 0 1, 0, 0, 0, 0 0 0, 0, 0, 0, 0, 0, 0,0

If a user were now to select the data element value of “Accord” in thedata entry type “Model” the above process would be repeated to reflectthis additional criteria in the query. The only remaining record whichmatches all three of these criteria is record 3. Record 13 matches“Accord” as well as “Honda,” but no other matches with “Accord” shall befound. It is important to note that after processing the first query, itis already known which records match the criteria of that first query;additionally, the result of this new query will be similar to theprevious query except that an extra Boolean digit will be appended toall of the status vectors according to whether a match with “Accord” isfound in the corresponding records. It is therefore possible (withoutdeparting from the scope of the present application), but not required,to use the status vectors resulting from that first query as input tothe logical inclusive OR operation as described above instead ofcarrying out the steps required to evaluate the criteria common to boththe first and subsequent queries.

It is therefore possible to determine which records match the query bycomparing the various status vectors. The above described vectors canalso be associated with an identifier (typically an identifierassociated with the stored query vector) and stored in memory to berecalled whenever the same query is repeated. For example records 1through 5 all include “Coupe” within the “Type” data element type, andthe remaining records do not include a “Coupe” within the “Type” dataelement type, while only the first three records match both “Coupe” and“Honda.” Similarly the stored status vectors for the other data elementtypes will indicate the data element values for each of these types,within the various records. In this manner if the query is reprocessed,the results are already known and can be retrieved with minimaladditional processing whenever the same query is reused. It should benoted that the query itself (or its identifier) can also be consideredthe unique identifier for the results, since the intent is to associatethe query with the results of that query because unless there is achange in the underlying data, a given query will always yield the sameresults. Once the query/result combination is cached or stored inmemory, it can easily be retrieved if the query is repeated, without aneed for additional processing to recalculate the results.

It should be noted that the query and query results can be storedindependent of the initial and final data structures which reflect thedata in the database, and also independent of the actual data in thedatabase. Independent storage and retrieval of these elements enablesstorage of multiple query/result combinations. Each of thesequery/result combinations can represent a particular data state, andaccordingly multiple data states can be stored, including the initialstate in which no query or data selections have been made. Furthermore,each of the data states represented by the stored structures and vectorscan be easily recalled and used as desired. In particular, in anembodiment of the present disclosure, the stored structures and vectorscan be represented by various visualizations.

An embodiment of the disclosure can include the creation and display ofvisualization objects (such as graphs, charts, list boxes, tables, etc)for at least each of the data element types comprising the query, as isillustrated in FIG. 6. As described further with respect to FIG. 6, thenon-shaded values represent the exact match all of the criteria, thelight grey shaded values match one or more, but not all of the querycriteria, and the dark grey shaded values do not match any of thecriteria. Although not shown, it is apparent that the stored vectors canalso show the records matching only the second criterion and not thefirst, and vice versa. The teachings of the present disclosure enablethe user to determine “degrees of query match.”

As is apparent from FIG. 6, the original alpha-numeric values for theapplicable data element types and data element values have been restored(e.g., instead of the assigned binary codes) utilizing the initial datastructures. Also as is apparent from FIG. 6, list boxes for other dataelement types can also be displayed. These list boxes are able toinclude not only the data element values matching the query, but alsothe data element values that would have been normally been excluded as aresult of the query, absent the teachings of the present disclosure.Again, for the purpose of enabling a better understanding, the dataelement values that were the subject of the query have been highlightedin green, the results matching all of the elements of the query areillustrated without highlighting, the results matching the element ofthe query that was first entered but not one or more other elements ofquery are illustrated with a light grey shading, and the results whichdo not match any elements of the query are illustrated with dark greyhighlighting.

In an aspect, the methods and systems can utilize various pre-defineddatabase objects, such as various charts, graphs, tables, list boxes,etc., that can be represented by the static information needed to renderthe objects, such as the object type, name, format, shape, elements,etc. This static information can be contained in javascript, css or htmlcoding that is read by the rendering engine as required to render thebasic object. In addition each object can include certain dynamicinformation, which can be user specified, such as the applicable labels,titles, orientations, colors, dimensions, measurements, sort orders,etc. Finally in order to actually render the final object, the actualdata or data state to be visualized with the object can be specified. Inan aspect, the information required for rendering an object can includecalls to the applicable static information for that object, as well asto the vectors associated with the particular data state for the object.This information can be combined with the specified dynamic informationwhen the object is created, to fully render the object. As can beappreciated, the data and static information required to render aparticular visualization need only be specified when the visualizationis created. It can then be associated with an applicable hash functionand stored. Accordingly, when an object is specified by a user, anembodiment of the present methods and systems can create a hypercube orother data structure that includes the data and static information alongwith the calls necessary to retrieve and utilize the static informationand data state necessary to render the object. For convenience the termhypercube will be used for future reference to this data structurealthough other data structures could be utilized. Typically a user willspecify more than one object in order to visualize and understand thedata being displayed. In this case the entire collection of objects, aswell as each individual object will be associated with a uniqueidentification code or hash function and stored in a manner that it canbe recalled. Thus each object as well as each collection of objects canbe easily re-created or reproduced merely by referencing the applicablehash function, and without requiring the reprocessing of any data. Inaddition multiple collections of visualization can easily be stored andretrieved.

Through the teachings of the present disclosure therefore, with theseparate storage and easy retrieval of the individual data structures,vectors, and visualization hypercubes, apart from the actual data it ispossible to create multiple data states and multiple visualizationstates, all of which can be separately stored and easily recalled. Andthis can be done without the underlying database being changed, eitherwhen the states or presentations are created or when they are called.Rather the data states and visualizations are stored as independentvectors and hypercubes, so additional states and visualizations can beadded and stored as additional vectors and hypercubes, each of which canbe independently recalled and utilized. Furthermore, security andprivilege features can be added to control the users and conditionsunder which the various vectors and hypercubes can be called, used ormodified. Furthermore, the present methods and systems can identify thevarious vectors and hypercubes by time, state, user, point of focus,queries represented, and other criteria. For example a particular datastate and visualization can be set so that it only can be recalled andused by identified users. Similarly a state and visualization can be setso that it reflects a particular time period or focus.

In addition, an embodiment of the present methods and systems alsoallows a user to associate “weights” with specific query criteria and/orwith the number of criteria matched since the vectors reflect not onlywhich data element types and data element values were matched, but alsothe number that were matched. The frequency vectors can be used for thepurpose of determining the number that were matched. As is apparent fromFIG. 5, data records 1-5 have Coupe (data element value “0”) as theirType, and records 1-3 and 13 have Honda (data element value “3”) astheir Make. If this were a search using the techniques of the prior art,the results of the search would yield only the data associated with thethree data records since these are the only vehicles that are bothCoupes and Hondas. All the rest of the data would be excluded with theseprior art techniques. However with the present methods and systems thesedata are not excluded and remain available for analysis as illustratedin FIG. 5.

Accordingly, and as is illustrated in FIG. 5 and FIG. 6, vehicles thatare both Coupes and Hondas have either a 1.8 or 2.4 engine,transmissions that can be either automatic or manual, a price of$18,000; $19,000; or $23,000, and are either an Accord or a Civic. Thesealternatives are shown without any shading. Using the techniques of thepresent disclosure, FIG. 6 however, contains substantially moreinformation, and this information is displayed in a manner that does notobscure the result set that totally matches the search criteria (i.e.,the values displayed without shading.)

FIG. 6, therefore, includes data element values displayed in a lightgrey shading (e.g., Engines having values 3.6 and 3.7) and in dark greyshading (e.g., Engines having values 1.6, 2.0, 2.5 and 3.5). Referringto Tables 1-8, it can be determined that Coupes (Type 0) can haveEngines with the binary codes 1, 3, 6, or 7 which have been shaded inblue in FIG. 5 (or with the actual values 1.8, 2.4, 3.6, and 3.7), butdo not have Engines with the binary codes 0, 2, 4, or 5 (or with theactual values 1.6, 2.0, 2.5, or 3.5). Similarly Hondas can have Engineswith the binary codes 1 and 3 which have also been shaded in blue in thefigure (or with the actual values 1.8 and 2.4) but do not have Engineswith the binary codes 0, 2, 4, 5, 6, or 7 (or with the actual values1.6, 2.0, 2.5, 3.5, 3.6, or 3.7.) Comparing both Hondas and Coupes theycan both have Engines with the binary codes 1 and 3 shaded in the darkerblue in the figure (or with the actual values 1.8 and 2.4.) However,Honda Coupes do not have Engines with the binary codes 0, 2, 4, 5, 6 or7 (or with actual values 1.6, 2.0, 2.5, 3.5, 3.6, or 3.7). Note thatwhile Coupes do have Engine sizes with binary codes 6 and 7 (or withactual values 3.6 and 3.7), Hondas do not. The blue shaded cells in FIG.5 include the first three rows of values (not including the header row),but excluding the values in the “Type” and “Make” columns.

FIG. 6 makes this clear by shading the values which match some, but notall of the criteria in a lighter shade of grey (e.g., engines that are3.6 or 3.7 in size, and values which do not match any of the criteria ina darker shade of grey (e.g., Engines that are 1.6, 2.9, 2.5, or 3.5 insize). Shading is only one way, of many ways, to visually indicate thesecharacteristics and other graphic or informational treatments thatpermit a display of totally matching, partially matching, and notmatching are all within the scope of the present disclosure.

As shown in FIG. 3c , utilizing the frequency vectors described above itis also possible to count such things as the number of records matchingone or more data element values of the query, the number of data elementtypes and/or data element values of the query that have been matchedwithin a record (and/or in the aggregate) the frequency of each of thedata element values (matched or unmatched) appearing within the matchingrecords and/or the unmatched records, and so forth; and to display theresults of such counting operations. Various embodiments of the presentdisclosure can include any combination of these and other counts. Thedisplayed data element types and/or data element values can be sorted bytheir frequency of occurrence in the result data object or by othersorting parameters. With one or more embodiments of the present methodsand systems this sorting can be facilitated through the use of theassigned binary codes.

At step 301 c, for the identified records, the methods and systems canidentify the data element types that contain the relevant data elementvalues and count the number of unique data element values within in eachdata element type. At step 302 c, the methods and systems can displayresulting data, such as the names of the data element types that containrelevant data element values, the unique data element values in each ofthe displayed names of the data element types and a count (See FIG. 7)of the number of occurrences of such data element values. At step 303 c,the methods and systems can adjust the query (e.g., return to step 301 bof FIG. 3b ) in the event of any selection or de-selection of adisplayed data element type or data element value.

For example in Table 8e it is evident that there were five recordsmatching the data entry type “Coupe,” of which with respect to dataentry type “Engine,” two had a value of “1.8,” with one record each forthe values “2.4,” “3.6,” and “3.7.” Similarly there are three recordshaving a Manual transmission and two records having an automatictransmission. Furthermore, after the selection of Honda, there were nowonly 3 records which match both “Coupe” and “Honda,” of which withrespect to data entry type “Engine,” two had a value of “1.8,” with onerecord for the value “2.4.” Similarly there are two records having aManual transmission and one record having an automatic transmission. Anexample of a potential data display as a result of this countingoperation is shown in FIG. 7 wherein the names of the data element typesthat contain relevant data element values resulting from the query, aswell as the unique data element values for each of the displayed names,and a count of the number of occurrences of these data element valueswithin the database. FIG. 7 shows an updated example of FIG. 6 in whichtwo additional columns per list box have been added to display thenumber of occurrences of each data element value in each data elementtype (the first added column) and the number of query terms that suchdata element value matched. For example, with respect to the dataelement type “Engine,” the first data element value “1.8” was found intwo of the records (first added column) fully matching the querycriteria, and it was found in records matching both (second addedcolumn) “Coupe” and “Honda.” Contrastingly, a “2.4” engine was found inonly one record, but it still was a match in both query criteria. Itshould be noted that while the values in both of these columns can bedetermined from that foresaid vectors and data structures, these columnsneed not be displayed in a visualization, rather which of these columnis displayed is user configurable. The number within the added columnsof FIG. 7 can be used to sort the data element values within thevisualization further illustrating the “degrees of query match”indicating by the various shaded areas. In this example “Manual” can besorted above “Automatic” in the Transmission field. In this example thelist boxes are first sorted by “degrees of query match” then number ofoccurrences of a value in a given field followed by alphabetical ornumerical sorting depending on the data type and preference. Thissorting order is again user configurable and all sorting orders areconsidered to fall within the scope of the present disclosure.

Utilizing the teachings of the present disclosure, the process ofquerying a database can be substantially more robust, since all of thedata element values in the database remain available for analysis. Fromthe data display of FIG. 7 it is possible to view not only the directresults of the query but also the results which closely match the queryalong with a count indicated the degree of the match. For example, witha query that uses three data element types it is possible to see resultswhich only match one or two of the results, and so forth. In FIG. 7 thedata element types that are the subject of query appear in a selectedcolor or graphic treatment, in this instance, they are shaded green (thefirst row in the “Type” table and the first row in the “Make” table).The data element values which completely match the query appear in acontrasting color or graphic treatment, in this instance, they are notshaded (or have a white background). The data element values which donot match any of the query requirements appear in yet anothercontrasting color or graphic treatment, in this instance, they areshaded dark grey. The data elements which match some, but not all, ofthe query requirements appear in yet another contrasting color orgraphic treatment, in this instance, they are shaded a lighter shade ofgrey. In addition FIG. 7 also indicates the number of query criteriaactually matched.

FIG. 8 is a similar visualization which in this instance illustrates notonly the number of records which were matched by the query criteria, butalso which of the data element types within the query provided thematch. Again, the particular information which is displayed is userconfigurable.

This ability to see closely matching data as well as matching datafacilitates better decision making. For example if a user searched atravel database for flights leaving from Chicago going to New York, onUS Air, and leaving before 8:00 AM on Monday, the prior art databasesystems would only display the matching flights, while with theteachings of the present disclosure, alternative flights from otherairlines (for example matching the city and departure criteria but notthe airline criteria) could be displayed. Similarly a user can bepresented with flights not only from other airlines but also flightsleaving at 8:15 AM.

In addition, since all of the data in the database remains available foranalysis, the queries being made can be changed dynamically and in realtime, with the results of the query being updated, also dynamically andin real time. Query changes may also include selection or de-selectionof any of the displayed data element types or data element values. IfQuery changes do occur, one or more steps of FIG. 3b and/or FIG. 3c canbe repeated as needed.

Furthermore in an embodiment of the present disclosure, priority valuescan be assigned to the data element types and data element values usedwithin the query. Such data priority values can be used to manipulatethe sort order, graphic and/or informational treatment applied to dataelements when they are displayed by the techniques of the presentdisclosure. For example with the travel database the departure time canbe weighed heavier than the airline, and the result set will be adjustedaccordingly.

Returning to the previous automobile example, an additional selectioncan be made in Transmission type by selecting the value “Manual.” Sincethere are now selections in three separate fields there can be valuesthat match all three, two out of three, one out of three or none interms of “degrees of matching.” This has been illustrated in FIG. 9. Forexample if we now look at the Engine field we can see that 1.8 and 2.4match all the specified criteria. Engine size 3.7 matches both theselection in Type and Transmission (Trans.). The value 3.6 only matchesType. The values 1.6 and 2.0 match Transmission only. Similar toprevious examples the “degrees of matching” is represented by variousgraphic treatments like the illustrated gray shading. In this case 3shades are used: a lighter shade for those matching 2 out of 3; a mediumshade for those matching 1 out of 3; a darker shade for those matching0.

As discussed with the flight example some fields may be more importantto a given result set than others. For the discussed example in FIG. 9the field “Type” will take precedent, e.g. sorted first when thesituation arises. If the “Engine” field is reviewed again the followingsort order can apply: sort by “degree of matching” indicated by the grayshading and sort by the preferred field in this case “Type.” In the“Engine” field there are 3 values that match only one field 3.6, 1.6,and 2.0. The value 3.6 is sorted first in this list as the system hasbeen defined to give preference (or a higher weighting) to the “Type”field. Since 3.6 matches the “Type” field and 1.6 and 2.0 match the“Transmission” field 3.6 is sorted first.

The weighting can be a list of field names or even a combinationthereof. So for example given such a dataset the combination of Type andTransmission (Type, Trans.) can take priority over Type and Make (Type,Make). Additionally the weighting can be user-configurable either beforeor during data analysis. A desired priority for some or all field namesor combinations thereof may be configured on a temporary or permanentbasis which is to be used for all queries. Additionally oralternatively, a user can adjust the priority of fields based uponfields selected in the current query. This priority can be achieved bymultiple means including the order in which the criteria are specifiedor as part of the definition of the particular visualization object.

Furthermore in an embodiment of the present disclosure, it is possibleto perform free-form searches in which the data element values and thedata element types are searched for a particular search term in order tofind those data records matching either all or particular combinationsof the search terms. Boolean type searches search all data element typesfor each individual search term entered. This is complex and consumessignificant computing resources. The teachings of the present disclosuregreatly simplify the process of performing free-form searches by takingadvantage of the separation and separate storage of the data elementtypes and the data element values from the data records, and bysearching data element types individually before finding an associationbetween the types according to the search criteria.

For example, if the free-form search query “automatic ford” is entered,the entry is typically interpreted as finding those data records whereone of the fields contains “automatic” and one of the fields contains“ford.” This is typically translated into a Boolean query such as (TypeBEGINS WITH “automatic” OR Transmission BEGINS WITH “automatic” OR MakeBEGINS WITH “automatic” OR Model BEGINS WITH “automatic”) AND (TypeBEGINS WITH “ford” OR Transmission BEGINS WITH “ford” OR Make BEGINSWITH “ford” OR Model BEGINS WITH “ford”). As can be seen such an entryis complex and will consume significant resources to process. Thecomplexity of such a query increases with the number of fields andsearch terms involved, usually rendering such free-form searchesimpractical or computationally expensive.

This is not the case according to the teachings of the presentdisclosure. For simplicity, if the query criteria are alphabetic, thenan embodiment of the present methods and systems can exclude searchingthe numeric fields Engine and Price. An embodiment of the presentmethods and systems can also start a free-form search by searching eachfield individually for each individual search term or phrase, creating astatus vector for the field representing the data element values thatmatch one or more of the search terms. The Boolean query used todetermine the matching data records is the logical conjunction of thefree-form searches for each search term, the free-form searches being alogical disjunction of searches of each field for the search term; theBoolean query is one such example of this form of query. Notably, themethods and systems do not have to evaluate the entire query for eachrecord or data element value, but instead specific expressions withinthat query. The Boolean strings in all of the status vectors must belong enough to contain the result of each and every Boolean expressionnecessary to compute the entire Boolean query correctly and withoutambiguity; a minimal expression that will satisfy this property is abinary operator accepting one of the search terms and a value of a dataelement type. Two examples of such expressions are ‘Model BEGINS WITH“ford”’ or ‘Engine>2.0’. Each of these Boolean expressions is assigned aunique and subsequent position in the Boolean strings in the same waythat data element types are assigned such positions as described above.At the end of this process every Boolean expression in the query must bevalid. The definition of a valid Boolean expression is recursive anddefined by the following criteria (a) it has been assigned a position inthe Boolean strings in the status vectors, and (b) it must be a logicalconjunction, disjunction or negation of valid Boolean expressions.

Each data element value in each data element type can be processed,either evaluating each Boolean expression against the value or ignoringit if the expression does not refer to this data element type, then aBoolean string can be generated consisting of the results of theexpressions (where true corresponds to 1 and false or ignored to 0) andstored in the status vector at the position corresponding to the dataelement value. Using the example data set and search terms, since thereare 8 minimal Boolean expressions required to compute the entire Booleanquery, one for each search term/data element field combination, eachBoolean string in the status vectors is 8 digits long. There are onlytwo data element values for which one of the Boolean expressionsevaluate to true: “Automatic” in Transmission and “Ford” in Make. Usingthe same order as given in the example Boolean query, the second bit(corresponding to the Boolean expression “Transmission BEGINS WITH‘automatic’”) can be set to 1 in the string corresponding to“Automatic”, and the seventh bit (corresponding to the Booleanexpression ‘Make BEGINS WITH “ford”’) can be set to 1 in the stringcorresponding to “Ford”. The resulting status vectors representing thissearch are set forth in Table 9a.

TABLE 9a Status Vectors for the various data element types after receiptof free- form search query “automatic” AND “ford” Type EngineTransmission Price Make Model 00000000, 00000000, 01000000, 00000000,00000000, 00000000, 00000000 00000000, 00000000 00000000, 00000000,00000000, 00000000 00000000, 00000000, 00000010, 00000000, 00000000,00000000, 00000000, 00000000, 00000000, 00000000, 00000000 00000000,00000000, 00000000, 00000000, 00000000, 00000000, 00000000, 0000000000000000, 00000000, 00000000, 00000000, 00000000, 00000000, 0000000000000000, 00000000, 00000000

Having created the initial status vectors for each data element type,the query can be calculated, resulting in the table status vectors shownbelow in Table 9b. Table 9c illustrates the final status vectors for thedata element types after traversing all of the records.

TABLE 9b Initial table status vector After receipt of free-form searchquery “automatic” AND “ford”: 01000000, 00000000, 00000000, 01000000,00000010, 00000010, 01000000, 01000000, 01000000, 00000010, 01000000,01000010, 00000000, 01000010, 01000000, 01000010

TABLE 9c Final Status Vectors after traversing all records in Table 8Type Engine Transmission Price Make Model 01000000, 00000010, 01000010,00000010, 01000000, 00000000, 01000000, 01000010, 00000010 01000000,01000000, 01000000, 01000010 00000010, 00000000, 01000010, 01000000,00000000, 01000000, 01000000, 01000000, 01000010, 01000000, 0100000000000010, 01000010, 01000010, 00000010, 01000000, 01000000, 01000010,00000010 01000000, 01000010, 01000010, 01000000, 01000010, 00000010,01000000 01000000, 01000000, 01000010

It is therefore possible to determine whether a data record or dataelement value matches the Boolean query by retrieving the correspondingBoolean string from the corresponding status vector, substituting eachBoolean expression in the query with the true or false value at thecorresponding position in the Boolean string, and evaluating the query.For example, the first record in the table has the Boolean string01000000, meaning only the second Boolean expression (TransmissionBEGINS WITH “automatic”) is true. Replacing the expressions in theBoolean query with their corresponding results gives the expression (0OR 1 OR 0 OR 0) AND (0 OR 0 OR 0 OR 0)=0, so it is known that the firstrecord does not meet the search criteria. However, the twelfth recordhas the Boolean string 01000010, and using the same process theexpression (0 OR 1 OR 0 OR 0) AND (0 OR 0 OR 1 OR 0)=1 can be obtained;this record matches all criteria. The same process can be carried outfor the data element values to determine which values of which dataelement types match the search terms.

An extension of the present disclosure can use these status vectors tocompute additional instances of certain Boolean queries efficiently, todetermine the number of search terms matched by records or data elementtypes, and/or to indicate which combination of fields contain a validassociation. This is possible because only the expensive Booleanexpressions that identify which fields match which search terms arecomputed once, reducing the original Boolean query and any subsequentqueries comprising the same expressions to a series of bitwiseoperations which can be computed extremely quickly.

To determine the number of search terms associated with a record,Boolean queries can be created for each search term comprising thelogical disjunction of all Boolean expressions relating to that searchterm; the number of queries that evaluate to true corresponds to thenumber of matching search terms.

Each combination of fields can be found that comprise a validassociation by storing each distinct Boolean string in the table statusvector for which the original Boolean query evaluates to true.Determining the fields participating in a valid combination is done byfinding each digit of the corresponding Boolean string that equals 1,then obtaining the field referred to in the Boolean expressioncorresponding to that digit.

If it is not necessary to know which of the Boolean expressions matchedparticular data records or data element values, a particular embodimentof this disclosure can manipulate the initial status vectors bycoalescing the Boolean expressions which are subject to logicaldisjunction in the Boolean query. By reducing the number of expressionswhich have to be represented in the status vectors, the total space andcomputation required can also be reduced. The correctness of the queryis preserved because the Boolean strings in the status vectors arethemselves subject to logical disjunction during the processing of thetable status vector, making them logically equivalent.

It is also possible to apply constant propagation to the Boolean queryprior to calculating the first table status vector, eliding thecalculation of expressions that will always evaluate to either true orfalse. Determining whether an expression is constant can be achievedeither through prior knowledge of the data set (for example, if themaximum of the values of a data element type is known, the result of allcomparisons with a value greater than that maximum can be proved withoutlooking at any other values) or after processing some or all of thevalues to conclusively determine the truth of the expression.

Similarly, it is also possible to use “auto-complete functionality tofacility the entry of search terms for the same reason. Again, using thestored data structures it is possible to dynamically search the contentof these structures as entries are being made for the desired searchcriteria.) Using the discussed methods a Free Form Search with word orphrase auto-complete functionality can be improved by taking intoaccount the search entries in the search field and ranking auto-completesuggestions based on “degrees of query match”.

FIG. 10a shows a simple auto-complete suggestion. The user enters “To”and all values in all fields or a sub-set of fields specified by theuser are searched. In this case “To” only matches Toyota and this issubsequently suggested. In FIG. 10b the user has already entered“Toyota.” The user then starts typing the letter “A” and new suggestionsare given in the auto-complete functionality. Since the methods andsystems are able to find all the combinations of “Toyota” and wordsbeginning with the letter “A” using the “degrees of query method” themethods and systems are able to rank suggested words in theauto-complete higher if there is relationship or association in theunderlying dataset. In this example the word “Automatic” is suggestedfirst since the methods and systems know from interrogating the datasetthat there is a valid relationship or association in the data. Thisassociation is displayed, in this example, by showing both search termswithin the same shading. This embodiment continues with a display of theindividual fields which are matched and “Accord” and “ATS” are displayedwith a lower preference in the order since there is no relationship orassociation between “Toyota” and “Accord” or “ATS.”

In the results section of the search the valid combinations of relatedor associated fields and corresponding values are shown. In this example“Toyota” in the field “Make” and “Automatic” in the Field—“Transmission”is the only valid combination that has matches in two separate distinctfields with a valid relationship or association. Furthermore, resultsmatching only one term at a time are also shown.

FIG. 11 shows an example when the user enters “Automatic H” in thesearch field. In this example there are two results that match the twoentered words in two distinct fields. The first result finds a validrelationship or association between “Automatic” in the Transmissionfield and “Honda” in the Make field. The second result finds a validrelationship or association between “Automatic” in the Transmissionfield and “Hatchback” in the Type field. Using the above methods thesituation can occur when a word or partial word match matches in morethan one field and a valid association is found in both of thesematches. For example consider a product dataset with Product Category,Product Subcategory and Product data element types, and “Bike” as avalue for Category, “Mountain Bike” as a value for the sub-category and“Bike 200L” as a value for the Product. If the user now searched “Bike”with the method described a result would find an association between“Bike,” “Mountain Bike,” and “Bike 200L.”

A more targeted result would be to limit the amount of valid searchresult combinations to searching the identical word (in this case“Bike”) in only one field instead of many. To illustrate this using theprevious examples shown above FIG. 12a demonstrates what would happenwithout this improvement. The user searches for “Co” and a validrelationship or association between the value “Coupe” in Type and“Corvette” in Model is shown. With the improvement shown in FIG. 12b itcan be seen that this combination is not shown or eliminated leading toa better user experience.

If the user is interested in searching for relationships or associationsacross multiple fields then the user can explicitly enter the searchterms for the number of field combinations they are looking for. Forexample in FIG. 12c the user has entered “Co Co” and the result getscombinations which have a valid relationship or association across twodifferent fields.

In an aspect, illustrated in FIG. 13a , provided are methods foranalyzing information within a database. The database can comprise oneor more database structures which collectively contain a plurality ofdata records. Each record can have at least two data element types, withat least one of the data element types having a different data elementvalue from the data element value for the corresponding data elementtype in at least one other record in the database. The methods cancomprise reading the plurality of records at 1301, instantiating aninitial data structure for each unique data element type within theplurality of records at 1302, creating an entry in the initial datastructure for each data element type for each unique data element valuewithin that data element type at 1303, selecting one or more databasestructures within the database at 1304, and instantiating a final datastructure for the selected database structures in which the data elementvalue for each data element type reflects the entry made in the initialdata structures for that data element value at 1305.

In an aspect, illustrated in FIG. 13b , the methods can furthercomprise, receiving a query for information from the database at 1306,determining particular data element types and data element values thatare the subject of the query at 1307, instantiating a query datastructure containing the data element types and the data element valuesthat are the subject of the query at 1308, identifying records withinthe database that contain one or more data element types and/or dataelement values that are included in the query data structure at 1309,and instantiating a results data structure comprising informationrelating to the identified records at 1310. Instantiating a query datastructure can comprise using one or more values that reflect the entrymade in the initial data structures for the data element. Instantiatinga results data structure comprising information relating to theidentified records can comprise indicating in the information whether ornot the data element types and data element values of the identifiedrecords were included in the query data structure.

The methods can further comprise assigning a unique code, which code isof a type that can be used to facilitate computer processing, to eachunique data element value within each data element type, wherein theentries in the initial data structures and the query data structure forthe data element values are their assigned code instead of the actualdata element values. The methods can further comprise associating aunique identifier to each of the initial, final, query and results datastructures, and storing in memory the initial, final, query and resultsdata structures along with their respective unique identifier.

The methods can further comprise displaying one or more of the dataelement values in a manner that indicates whether or not the dataelement values were included in the query data structure. The methodscan further comprise counting the frequency of occurrence of each of thedata element values included in the results data structure, and usingthe results of such counts to prioritize such data element values. Themethods can further comprise sorting the displayed data element valuesincluded in the results data structure by the order of their frequencyof occurrence. The methods can further comprise utilizing the number ofoccurrences to provide suggestions for the values for alternativequeries of the database. The methods can further comprise counting thenumber of unique data element values included in the results datastructure and utilizing such count to further analyze the informationcontained the database. The methods can further comprise counting thenumber of records containing one or more of the data element valuesincluded in the results data structure and utilizing such count tofurther analyze the information contained the database. The methods canfurther comprise counting the number of records that do not contain oneor more of the data element values included in the results datastructure and utilizing such count to further analyze the informationcontained the database.

The methods can further comprise using values in the initial databasestructures to suggest values for the query criteria during the entry ofvalues for queries of the database, thereby enabling the user to acceptsuch suggested values without actually entering the full query values.The methods can further comprise using values in the initial databasestructures to determine whether the values being entered for a query areeither data element types or data element values and thereby affordsearches of the database without the specification of whether theentered query values are data element types or data element values.

In an aspect, illustrated in FIG. 14, provided are methods for analyzinginformation, comprising identifying, in a database, unique data elementtypes at 1401, generating a plurality of initial data structurescorresponding to the unique data element types at 1402, wherein theplurality of initial data structures comprise unique data elementsassociated with the corresponding unique data element type, generating afinal data structure based on the plurality of initial data structuresat 1403, wherein the final data structure is a non-redundant combinationof the plurality of initial data structures, and providing searchresults relevant to a search query based on the final data structure at1404.

In an aspect, generating the final data structure based on the pluralityof initial data structures can comprise generating binary codes forcorresponding unique data elements. Generating the final data structurebased on the plurality of initial data structures can comprise combiningat least two of the plurality initial data structures. Combining atleast two of the plurality of initial data structures can compriseresolving at least one of data element dependency and data elementredundancy between the at least two of the plurality of initial datastructures.

In a further aspect, illustrated in FIG. 15, provided are methods,comprising receiving a query for information from a database at 1501,identifying data element types relevant to the query and data elementvalues relevant to the query at 1502, generating a query structure basedon the identified data element types and data element values at 1503,identifying a data structure relevant to the query structure at 1504,wherein the data structure comprises non-redundant data element valuesfrom the database, identifying records in the data structure based onthe query structure at 1505, and providing the records in response tothe query for information at 1506.

In an aspect, identifying the data structure relevant to the querystructure can comprise identifying a data structure comprising thegreatest number of identified data element types relevant to the query.Identifying records in the data structure based on the query structurecan comprise identifying records associated with at least one of thedata element types relevant to the query and the data element valuesrelevant to the query. Providing the records in response to the querycan comprise displaying at least one of unique data element types,unique data element values, and a count of the number the unique dataelement values.

In an aspect, one or more database snapshots can comprise theinformation needed to render images such as visualization hypercubes orother visualizations or portions thereof. In another aspect, the one ormore database snapshots can comprise the underlying vectors representingthe data states represented in the snapshot. Accordingly, snapshotsusing the teachings of the present disclosure are not just graphic filesbut rather dynamic displays of data states and visualizations that canbe accessed and modified by the user. In an aspect, users of snapshotsgenerated according to the present disclosure can, for example, convertbar charts into scatterplots, change labels and axis parameters, andmake other changes to the snapshots. Accordingly, snapshots madeaccording to the teachings of the present disclosure can be dynamic. Thesystems and methods of the present disclosure facilitate the capture ofnot only an image of a visualization but rather the initial structures,vectors and hypercubes that lead to such visualization. This can beaccomplished through snapshots of one or more of the objects of thevisualization. The snapshots can reflect the data state when taken.However, since the snapshots also include the applicable structures,vectors and hypercubes, the snapshots are dynamic and the user canchange what is being displayed when a particular snapshot is recalled.The user can drill down and find, as well as alter, the data orvisualization state of the snapshot. The snapshot can containinformation defining how a user can manipulate and interact with thesnapshot, applicable structures, vectors and hypercubes.

FIG. 16 illustrates a method for analyzing information within adatabase. In an aspect, the database can comprise one or more databasestructures, which can collectively contain a plurality of data records.As an example, one or more of the data records can comprise one or moredata element types. As a further example, at least one of the dataelement types can have a different data element value from the dataelement value for the corresponding data element type in at least oneother record in the database. In another aspect, the database cancomprise one or more query data structures which can comprise the dataelement types and the database element values and the functions that canbe the subject of a query. In a further aspect, the database cancomprise one or more results data structures which can compriseinformation relating to one or more identified records. Such informationcan include whether or not the data element types and data elementvalues of the one or more identified records were included in a querydata structure. In yet another aspect, the database can comprise one ormore visualizations of at least some of the information in the resultsdata structure.

In a further aspect, illustrated in FIG. 16, provided are methods,comprising identifying, in a database, unique data element types at1601, generating a plurality of initial data structures corresponding tothe unique data element types at 1602, wherein the plurality of initialdata structures comprise representations of unique data elementsassociated with the corresponding unique data element type, generating afinal data structure based on one or more of the records, at 1603,wherein the final data structure can be based upon the data elementvalues included in one or more of the records and the data elementvalues represented in the plurality of initial data structures. At 1604,first information can be determined. First information can relate tofirst data to be retrieved from the final data structure. The firstinformation can comprise identifying information relating to thelocation and/or specification of the first data. The first informationcan be located within a stored data structure such as the final datastructure. At 1605, second information can be determined. The secondinformation can relate to second data to be retrieved from a sourceexternal to the final data structure, the second information cancomprise identifying information relating to the location andspecification of the second data. At 1606, search results can beprovide. The search results can be relevant to a search query based onthe first information and/or the second information. At 1607, data(e.g., the second data) can be retrieved (e.g., from the database beinganalyzed or another source/database). At 1608, the search results can bedisplayed via one or more visualizations. Such search results caninclude the retrieved data.

In an aspect, generating the final data structure based on the pluralityof initial data structures can comprise generating binary codes forcorresponding unique data elements. Generating the final data structurebased on the plurality of initial data structures can comprise combiningat least two of the plurality initial data structures. Combining atleast two of the plurality of initial data structures can compriseresolving at least one of data element dependency and data elementredundancy between the at least two of the plurality of initial datastructures.

In an aspect, methods are disclosed for analyzing information within adatabase that comprises one or more database structures whichcollectively contain a plurality of records, with each record having atleast two data element types, and with at least one of the data elementtypes having a different data element value from the data element valuefor the corresponding data element type in at least one other record inthe database. In an aspect, methods can be characterized by one or moreof the steps illustrated in FIGS. 13a and 13b . In another aspect, asillustrated in FIG. 17, methods can comprise, receiving a query forinformation from the database at 1701, determining first informationcomprising particular data element types and data element values thatare the subject of the query at 1702, determining second informationrelating to data to be fetched from a source external to the final datastructure, wherein the second information comprises identifyinginformation relating to the location and specification of the data to befetched at 1703, instantiating a query data structure containing thefirst information at 1704, appending the second information to the querydata structure at 1705, identifying the records within the database thatcontain one or more database element types and/or database elementvalues that are included in the query data structure at 1706, fetchingdata using the second information at 1707, instantiating and storing inmemory a results data structure containing information relating to theidentified records including whether or not the database element typesand database element values of the identified records are included inthe query data structure at 1708, and appending the fetched data to theresults data structure at 1709. At 1710, information from the resultsdata structure can be displayed with the appended fetched data in one ormore visualizations. The visualization can be saved. In an aspect, thevisualization can contain information from the results data structurewith the appended fetched data.

Instantiating a query data structure can comprise using one or morevalues that reflect the entry made in the initial data structures forthe data element. Instantiating a results data structure comprisinginformation relating to the identified records can comprise indicatingin the information whether or not the data element types and dataelement values of the identified records were included in the query datastructure. Fetching the data can comprises fetching the data from thedatabase being analyzed and/or fetching the data comprises fetching thedata from a specified external source.

FIG. 18 illustrates a method for analyzing information within adatabase. In an aspect, the database can comprise one or more databasestructures, which can collectively contain a plurality of data records.As an example, one or more of the data records can comprise one or moredata element types. As a further example, at least one of the dataelement types can have a different data element value from the dataelement value for the corresponding data element type in at least oneother record in the database. In another aspect, the database cancomprise one or more query data structures which can comprise the dataelement types and the database element values and the functions that canbe the subject of a query. In a further aspect, the database cancomprise one or more results data structures which can compriseinformation relating to one or more identified records. Such informationcan include whether or not the data element types and data elementvalues of the one or more identified records were included in a querydata structure. In yet another aspect, the database can comprise one ormore visualizations of at least some of the information in the resultsdata structure.

At step 1801, at least one of the visualizations can be received oraccessed (e.g., recalled). In an aspect, the received visualization caninclude information relating to a results data structure associated withthe received visualization. At step 1802, a determination can be madewhether additional information is required to be accessed or retrieved(e.g., fetched) from another source. If additional information isrequired, identifying information for fetching the additionalinformation from the other source can be determined. At step 1803, theadditional information can be accessed or retrieved using theidentifying information. At step 1804, retrieved information can beappended to the received visualization.

Furthermore since each object within a visualization or document can beassociated with its own hypercube, individual elements (or collection ofelements) contained within any snapshot can be extracted and/or utilizedas part of a report or presentation. The extracted elements, for examplerecalled vectors and hypercubes, remain dynamic and can be modified bythe users. The generated reports can be converted into staticpresentation such as a PowerPoint™-like presentations with addednarrative or graphic treatment. Accordingly, using the teachings ofpresent disclosure, snapshots can be shared by other users of productsin their dynamic, modifiable state and/or can be turned into staticrepresentations and included with traditional static reports andpresentations.

In a further aspect, illustrated in FIG. 19, provided are methods,comprising receiving a query for information from a database at 1901,identifying data element types relevant to the query and data elementvalues relevant to the query at 1902, generating a query structure basedon the identified data element types and data element values at 1903,identifying records in the data structure based on the query structureat 1904, generating a results structure, at 1905, selectively capturingone or more data states based upon the query structure and resultsstructure, wherein the one or more data states reflect a current datastate at 1906, and generating a current state structure for the capturedone or more data states at 1907. At 1908, one or more data statesrepresented by the query structure and results structure can beselectively displayed. At 1909, the displayed one or more data statescan be selectively captured and/or saved. At 1910, a displayed elementsstructure can be generated for the captured displayed data states forsubsequent retrieval.

In an aspect, identifying the data structure relevant to the querystructure can comprise identifying a data structure comprising thegreatest number of identified data element types relevant to the query.Identifying records in the data structure based on the query structurecan comprise identifying records associated with at least one of thedata element types relevant to the query and the data element valuesrelevant to the query. In an aspect, one or more of the query structureand the results structure can represent a data state. Providing therecords in response to the query can comprise displaying at least one ofunique data element types, unique data element values, and a count ofthe number the unique data element values.

In an aspect, previously stored visualizations or data states can bemodified. In another aspect, the modification can be previewed withoutsaving the changes or otherwise altering the originally stored datastates or visualizations. Since multiple data states and/orvisualizations can be separately stored and recalled, the user candecide whether to discard any changes made to these data states orvisualizations, or to save the changes made to the data states andvisualizations. The modifications can be saved to one or more of theoriginal and new data state or visualization. Depending upon theselections exercised, new or modified hypercubes or vectors can be savedalong with the unique associated identifying information. While theeffect of these changes can be viewed as part of a new data state orvisualization, they can easily be undone without saving the newresultant data state or visualization. In practice the preview mode canbe set up with scripting provided as part of an embodiment of thepresent disclosure. For example, a command could be issued to “SetupPreview” which would take a snapshot of the then current data stateand/or visualization, and store the snapshot along with the identifyinginformation need to recall it. Any changes which are now made will notbe reflected in the stored state that was called prior to making anychanges, although they can be viewed by the user as modified. Anothercommand, for example “AcceptPreview” can be used to save the currentstate, while a command such as “AbortPreview” can be used to undo thechanges without saving the state. Similar command syntax can be used tospecify whether the modifications are save as a new state orvisualization, or whether the state or visualization that was called ismodified and then saved. It should be recognize that other techniquesare available to determine whether a given state is saved or discarded,such as dragging and dropping. These other techniques are considered tobe part of the present invention.

In an aspect, methods are disclosed for analyzing information within adatabase that comprises one or more database structures whichcollectively contain a plurality of records, with each record having atleast two data element types, and with at least one of the data elementtypes having a different data element value from the data element valuefor the corresponding data element type in at least one other record inthe database. In an aspect, methods can be characterized by one or moreof the steps illustrated in FIGS. 13a and 13b . In a further aspect,illustrated in FIG. 20, provided are methods, comprising receiving aquery for information from a database at 2001, identifying data elementtypes relevant to the query and data element values relevant to thequery at 2002, generating a query structure based on the identified dataelement types and data element values at 2003, identifying records inthe data structure based on the query structure at 2004, and generatinga results structure, at 2005, the results structure containinginformation relating to the identified records selectively capturing oneor more elements of the query structure and results structure reflectinga current data state. At 2006, one or more first data states can becaptured based upon the query structure and results structure, whereinthe one or more first data states are an indicia of a current datastate. At 2007, one or more second data states of the query structure,the results structure, or both can be displayed. At 2008, the displayedsecond data states can be captured along with the first data states thatare the indicia of the current state. At 2009, the displayed data statescan be modified. In an aspect, the modified displayed data states can beselectively captured or discarded (e.g., modifications can be previewedand saved or discarded).

In an aspect, identifying the data structure relevant to the querystructure can comprise identifying a data structure comprising thegreatest number of identified data element types relevant to the query.Identifying records in the data structure based on the query structurecan comprise identifying records associated with at least one of thedata element types relevant to the query and the data element valuesrelevant to the query. Providing the records in response to the querycan comprise displaying at least one of unique data element types,unique data element values, and a count of the number the unique dataelement values.

In an aspect, information from the database can be added to a data stateor visualization that is not currently included in the recalled datastate or visualization. This added information can include an additionalfield or even a new table or other database structure. Typically thisinformation can be called or extracted from the database informationthat already exists but is not part of the present data state orvisualization. Again the user can determine whether the changes made aresaved as new states or visualizations or as modifications to the calleddata state or visualization, or discarded.

In another aspect, data that is external to (e.g., not part of) thedatabase and/or visualization can be retrieved. This can be done, forexample, by using SQL commands or statements. This aspect is not,however, limited to SQL syntax, and other syntax could also be used.This external data can be called and used as part of a new data state orvisualization, without changing the underlying database or the datastructures, or the vectors associated therewith. The fetched data can bedisplayed along with data from the database in any visualization of thedata. This ability leverages the other advantages of the presentdisclosure. In particular it provides the ability to fetch external datawhen the available memory is not sufficient or when data needs to beincluded from another database. Depending upon the exact commands orstatements utilized to fetch the external data, the fetched data can bejoined with the underlying initial or final data structures, or thevectors associated therewith for the stored database. When joined theuse can navigate the fetched data in the same manner as the stored data,and such fetch data can be included in visualizations of the data, allin a manner that is transparent to the user.

In an aspect, information contained in a data state or visualization canbe selectively included, discarded, and/or stored. The new or modifiedinformation can be previewed, discarded, and/or saved (e.g., saved tothe original data state or visualization or a new state orvisualization). Utilizing these teachings, the ability to addinformation to a data state or visualization and link that informationto one or more other data states or visualizations is possible. Withthese linked data states or visualizations changes that are made to one,can be carried over to the other linked data states or visualizations.The type or degree of linkage can be determined. For example the typesof changes that can be made by various types or identity of users can becontrolled. Master data states and/or visualizations can be implementedsuch that while changes in the master data state or visualization willbe carried over to the other linked states or visualization, the reverseis not true. Similarly one or more of the data states or visualizationcan be identified as being a master data state or visualization. Othertypes of linkage and linkage controls can be implemented and arecontemplated by the present disclosure, including but not limited tocontrolling such linkage by geographic location, time, type of license,and so forth.

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is no way intended thatan order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

What is claimed is:
 1. A system for analyzing information, comprising: afirst database comprising a plurality of records having one or more dataelement types and one or more data element values; and a computingdevice in communication with the database, the computing devicecomprising system memory and configured to: identify, in the database,unique data element types; generate a plurality of initial datastructure corresponding to the unique data element types, wherein theplurality of initial data structures comprise representations of uniquedata element values associated with the corresponding unique dataelement type; generate a final data structure based on one or morerecords of the plurality of records, wherein the final data structure isbased upon the data element values included in the one or more recordsand the data element values represented in the plurality of initial datastructures, wherein the final data structure comprises amultidimensional hypercube data structure; store the final datastructure in a portion of the system memory of the computing device;determine first information relating to first data to be retrieved fromthe final data structure; determine second information relating tosecond data to be retrieved from a second database external to thesystem memory, wherein the second information comprises identifyinginformation relating to the location and specification of the seconddata; joining the second data to the final data structure to generate anupdated data state without modifying the database; and provide avisualization comprising one or more graphical objects representing theupdated data state.
 2. A method for analyzing information within a firstdatabase that comprises one or more database structures whichcollectively contain a plurality of records, with each record having atleast two data element types, and with at least one of the data elementtypes having a different data element value from the data element valuefor the corresponding data element type in at least one other record inthe first database; the method characterized by the steps of: reading,by a first device, the plurality of records; instantiating, by the firstdevice, an initial data structure for each unique data element typewithin the plurality of records; creating an entry in the initial datastructure for each data element type for each unique data element valuewithin that data element type; selecting one or more database structureswithin the first database; instantiating, by the first device, a finaldata structure for the selected database structures in which the dataelement value for each data element type reflects the entry made in theinitial data structures for that data element value, wherein the finaldata structure comprises a multidimensional hypercube data structure;storing the final data structure in a portion of system memory of thefirst device; receiving a query for information from the first database;determining first information comprising particular data element typesand data element values that are the subject of the query; determiningsecond information relating to data to be fetched from a second databaseexternal to the system memory, wherein the second information comprisesidentifying information relating to the location and specification ofthe data to be fetched; instantiating a query data structure containingthe first information; appending the second information to the querydata structure; identifying the records within the first database thatcontain one or more database element types, database element values thatare included in the query data structure, or a combination thereof,fetching the data to be fetched using the second information;instantiating and storing in memory a results data structure containinginformation relating to the identified records including whether or notthe database element types and database element values of the identifiedrecords are included in the query data structure; joining the fetcheddata to the final data structure to generate an updated data statewithout modifying the first database; and providing a visualizationcomprising one or more data graphical objects representing the updateddata state.
 3. A method for analyzing information within a database thatcomprises one or more database structures which collectively contain aplurality of records, with each record having at least two data elementtypes, and with at least one of the data element types having adifferent data element value from the data element value for thecorresponding data element type in at least one other record in thedatabase, the method characterized by the steps of: reading theplurality of records; instantiating an initial data structure for eachunique data element type within the plurality of records; creating anentry in the initial data structure for each data element type for eachunique data element value within that data element type; selecting oneor more database structures within the database; instantiating a finaldata structure for the selected database structures in which the dataelement value for each data element type reflects the entry made in theinitial data structures for that data element value, wherein the finaldata structure comprises a multidimensional hypercube data structure;receiving a query for information from the database; instantiating andstoring in memory a query data structure containing the database elementtypes, the database element values and the functions that are thesubject of the query, using the entries stored in the initial datastructures for the data element values of the query; identifying datawithin the database that contain one or more database element typesand/or database element values that are included in the query datastructure; instantiating and storing in memory a results data structurecontaining information relating to the identified one or more recordsincluding the database element types and database element values of theidentified data; selectively capturing one or more first data statesrepresented by the query data structure and results data structure,wherein the one or more first data states are an indicia of the currentdata state, wherein selectively capturing comprises storing, for laterretrieval, a dynamic snapshot comprising 1) the one or more data statesat the time of the capturing and 2) a visualization comprising one ormore graphical objects representing the data, and wherein the one ormore graphical objects comprise one or more of a graph or a chart, andwherein a user can modify how the data is displayed by the visualizationby later interacting with the one or more graphical objects; selectivelydisplaying one or more second data states represented by the query datastructure and results data structure; selectively capturing thedisplayed one or more second data states along with the one or morefirst data states; and modifying the displayed one or more second datastates.